Dead-end Discovery: How offline reinforcement learning could assist healthcare decision-makers

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Microsoft Research
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Published on ● Video Link: https://www.youtube.com/watch?v=VedR00nUxSE


Duration: 8:07
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Microsoft Research Senior Researcher Mehdi Fatemi, MIT Assistant Professor Marzyeh Ghassemi, and PhD student Taylor W. Killian answer several questions about their NeurIPS 2021 paper, “Medical Dead-ends and Learning to Identify High-risk States and Treatments.” They discuss how their research in offline reinforcement learning and their AI model could alert a human decision-maker when a set of decisions are potentially high-risk, such as in healthcare scenarios.

Read the blog post here: https://www.microsoft.com/en-us/research/blog/using-reinforcement-learning-to-identify-high-risk-states-and-treatments-in-healthcare/

Read the full paper here: https://www.microsoft.com/en-us/research/publication/medical-dead-ends-and-learning-to-identify-high-risk-states-and-treatments/



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